1. Field of the Invention
This invention relates generally to signal classifiers, and, more particularly, to a signal classifier which employs fuzzy genetic learning automata.
2. Discussion of the Related Art
Typical passive sensor systems which collect wideband signals are receiving an enormous flux of information from a continually evolving signal environment. This enormous flux of information threatens to exceed the capacities of the human signal analysts to analyze the data. As a result, there is a pressing need for artificially intelligent systems which quickly analyze the arriving data stream, and, while so doing, effect machine learning in order to adapt to the continual signal-environmental changes. The intelligent systems should filter the signal data by identifying and separating known signals from the arriving flux of signal information. This potentially reduces the workload for the human signal analyst by allowing him/her to focus attention on signal features which are more likely to contain mission-important information.
Adaptive front-end signal classifiers are generally known in the art. Conventional front-end signal classifiers utilize many inner product-based schemes which compare the arriving signal to a set of basis functions that serve as exemplary signals. These inner product-based schemes may be based on Fourier analysis, wavelets, and/or Gabor transforms. In each of these schemes, the computational intensity is high and there is little flexibility to adapt to a changing signal environment.
Therefore, it is desirable to provide an adaptive signal classifier which learns to identify specific embedded spatiotemporal signals in an arriving signal data stream of an evolving signal environment. More specifically, it is desirable to implement a signal classifier using fuzzy automata. Fuzzy automata generally use min-max aggregation to process the incoming data, thereby obviating the need for highly intense inner product-based comparisons to exemplary signals. Since each fuzzy automata is a language-learning system, only one requirement needs to be imposed on the signals that are to be acquired: a signal needs to be characterizable as occurring in one or more states and transitioning as a whole or partially among some or all states over time. Thus, the formal language framework of fuzzy automata is particularly well-matched to signal separation applications. In addition, the operation of each fuzzy automaton is independent of the durations of the incoming signals.